Student thesis: Phd


Background: Endometrial cancer is the most common gynaecological malignancy in high-income countries and its incidence is rising. Early detection is key to ensuring good outcomes, but the absence of minimally invasive and highly accurate detection tools is a major barrier. The anatomical continuity of the uterine cavity with the lower genital tract allows for the exploitation of uterine-derived biomaterial in cervico-vaginal fluid for biomarker discovery. Plasma and urine are attractive biofluids for cancer detection due to their simplicity and ease of collection. Despite the generally favourable prognosis of endometrial cancer, a significant minority of women present with adverse clinico-pathological characteristics that portend poor outcomes. The aim of this study was to identify novel biomarkers that can improve diagnosis and risk stratification for treatment planning in endometrial cancer. Methods: 1) Matched Delphi screener-collected cervico-vaginal fluid, plasma and self-collected voided urine samples were obtained from symptomatic post-menopausal women with and without endometrial cancer. 2) A bespoke spectral library comprising 19,394 peptides and 2,425 endometrial cancer-related proteins was developed and validated for use in relatively quantifying and characterising putative biomarker signatures in cervico-vaginal fluid. 3) In a prospective case control study, digitised proteomic maps were derived for each sample using sequential window acquisition of all theoretical mass spectra (SWATH-MS). 4) Machine learning was used to identify the most important proteins enabling discrimination between cancers and controls. 5) Automated chemiluminescent enzyme immunoassay was used to evaluate the performance of urine CA125 and HE4 for endometrial cancer detection. 6) Non-targeted metabolomic analysis was performed on bio-banked plasma samples of women with BMI greater than 30kg/m2 with and without endometrial cancer and machine learning used to develop robust diagnostic models. 6) A large prospective database of endometrial cancer patients was used to evaluate the impact of systemic inflammatory parameters, immune-nutritional indices, area-level socioeconomic status and type 2 diabetes mellitus (T2DM) on endometrial cancer outcomes. Results: A protein signature derived from cervico-vaginal fluid more accurately discriminated between cancer and control samples than those derived from plasma or urine. A 5-biomarker panel of cervico-vaginal fluid derived proteins predicted endometrial cancer with an AUC of 0.95 (95% CI 0.91-0.99, p
Date of Award1 Aug 2023
Original languageEnglish
Awarding Institution
  • The University of Manchester
SupervisorAnthony Whetton (Supervisor) & Emma Crosbie (Supervisor)

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